{"id":929043,"date":"2023-03-21T21:03:30","date_gmt":"2023-03-22T04:03:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2024-08-27T11:44:51","modified_gmt":"2024-08-27T18:44:51","slug":"are-uglad-time-will-tell","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/are-uglad-time-will-tell\/","title":{"rendered":"Are uGLAD? Time will tell!"},"content":{"rendered":"

We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body movements. Segmentation of multivariate time series data is a technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system’s behavior. However, most segmentation algorithms have been designed primarily for univariate time series, and their performance on multivariate data remains largely unsatisfactory, making this a challenging problem. In this work, we introduce a novel approach for multivariate time series segmentation using conditional independence (CI) graphs. CI graphs are probabilistic graphical models that represents the partial correlations between the nodes. We propose a domain agnostic multivariate segmentation framework `tGLAD<\/span><\/span><\/span><\/span><\/span><\/span>‘ which draws a parallel between the CI graph nodes and the variables of the time series. Consider applying a graph recovery model\u00a0uGLAD<\/span><\/span><\/span><\/span><\/span><\/span>\u00a0to a short interval of the time series, it will result in a CI graph that shows partial correlations among the variables. We extend this idea to the entire time series by utilizing a sliding window to create a batch of time intervals and then run a single\u00a0uGLAD<\/span><\/span><\/span><\/span><\/span><\/span>\u00a0model in multitask learning mode to recover all the CI graphs simultaneously. As a result, we obtain a corresponding temporal CI graphs representation. We then designed a first-order and second-order based trajectory tracking algorithms to study the evolution of these graphs across distinct intervals. Finally, an `Allocation’ algorithm is used to determine a suitable segmentation of the temporal graph sequence.\u00a0tGLAD<\/span><\/span><\/span><\/span><\/span><\/span>\u00a0provides a competitive time complexity of\u00a0O<\/span>(<\/span>N<\/span>)<\/span><\/span><\/span><\/span>\u00a0for settings where number of variables\u00a0D<\/span><<<\/span>N<\/span><\/span><\/span><\/span>. We demonstrate successful empirical results on a Physical Activity Monitoring data.<\/p>\n

Software & demo: tGLAD (opens in new tab)<\/span><\/a><\/p>\n

Additional discussions: Tech Blog (opens in new tab)<\/span><\/a><\/p>\n

 <\/p>\n

\"tglad-flow-caption\"<\/p>\n","protected":false},"excerpt":{"rendered":"

We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body movements. Segmentation of multivariate time series data is a technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system’s […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Shima Imani","user_id":"42495"},{"type":"user_nicename","value":"Harsh Shrivastava","user_id":"41299"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Workshop on Advanced Analytics and Learning on Temporal Data (ECML 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